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One of the key challenges facing the professionalservices delivery business is the issue of optimally balancingcompeting demands from multiple, concurrent engagementson a limited supply of skill resources. In this paper, wepresent a framework for combining causal Bayesian analysisand optimization to address this challenge. Our frameworkintegrates the identification and modeling of the impact ofvarious staffing factors on the delivery quality of individualengagements, and the optimization of the collective adjustmentsof these staffing factors, to maximize overall delivery qualityfor a pool of engagements. We describe a prototype systembuilt using this framework and actual services delivery datafrom IBM’s IT consulting business. System evaluation underrealistic scenarios constructed using historical delivery recordsprovides encouraging evidence that this framework can lead tosignificant delivery quality improvements. These initial resultsfurther open up exciting opportunities of additional future workin this area, including the integration of temporal relationshipsfor causal learning and multi-period optimization to addressmore complex business scenarios.